SoroushVahidi/scidocs huggingface dataset:
Driver Sleepiness Detection System Based on Eye Movements Variables
(2015)

Driver sleepiness is a hazard state, which can easily lead to traffic accidents. To detect driver sleepiness in real time, a novel driver sleepiness detection system using support vector machine (SVM) based on eye movements is proposed. Eye movements data are collected using SmartEye system in a driving simulator experiment. Characteristic parameters, which include blinking frequency, gaze direction, fixation time, and PERCLOS, are extracted based on the data using a statistical method. 13 sleepiness detection models including 12 specific models and 1 general model are developed based on SVM. Experimental results demonstrate that eye movements can be used to detect driver sleepiness in real time. The detecting accuracy of the specific models significantly exceeds the general model (P < 0.001), suggesting that individual differences are an important consideration when building detection algorithms for different drivers.
Perceptual objects capture attention
(2009)

A recent study has demonstrated that the mere organization of some elements in the visual field into an object attracts attention automatically [Kimchi, R., Yeshurun, Y., & Cohen-Savransky, A. (2007). Automatic, stimulus-driven attentional capture by objecthood. Psychonomic Bulletin & Review, 14(1), 166-172]. We tested whether similar results will emerge when the target is not a part of the object and with simplified task demands. A matrix of 16 black L elements in various orientations preceded the presentation of a Vernier target. The target was either added to the matrix (Experiment 1), or appeared after its offset (Experiment 2). On some trials four elements formed a square-like object, and on some of these trials the target appeared in the center of the object. No featural uniqueness or abrupt onset was associated with the object and it did not predict the target location or the direction of the target's horizontal offset. Performance was better when the target appeared in the center of the object than in a different location than the object, even when the target appeared after the matrix offset. These findings support the hypothesis that a perceptual object captures attention (Kimchi et al., 2007), and demonstrate that this automatic deployment of attention to the object is robust and involves a spatial component.
Potential of flex-PCB winding in coreless tubular permanent magnet synchronous machines
(2016)

Many applications are limited by the performance of the motors that power them. In the small power range, slotless BLDC motors are commonly used due to their intrinsic high power density and smooth torque characteristic. However, recent work has shown that the power density of radial flux rotating motors could be significantly increased by replacing their windings, made of copper wire by windings printed on flexible PCB, taking most advantage of the possibilities offered this technology. The aim of this work is to evaluate the performance gain that could be obtained by using this technology in linear tubular motors. To that end, an analytical model is implemented and used for comparing different winding shapes regarding to two criteria, one related to the power density, the other to the force smoothness. A track shape which annihilates the higher harmonics is developed and compared to different 3-segment PCB windings and to a conventional wire winding.
Memory encoding in Alzheimer's disease: an fMRI study of explicit and implicit memory.
(2005)

Alzheimer's disease is the most common cause of dementia in older adults. Although the cognitive deficits and pathologic hallmarks of Alzheimer's disease have been well characterized, few functional imaging studies have examined the functional competency of specific brain regions and their relationship to specific behavioural memory deficits in Alzheimer's disease. We used functional MRI (fMRI) to examine seven early stage Alzheimer's disease patients and seven healthy age-matched neurologically normal control subjects during intentional encoding of scenes. Subjects viewed blocks of novel scenes, repeated scenes or baseline. Data were analysed using whole-brain statistical parametric mapping and region of interest approaches. The Alzheimer's disease group demonstrated impaired explicit recognition memory, but intact implicit memory (repetition priming), for the scenes. Alzheimer's disease patients demonstrated a graded deficit in activation for novel versus repeated scenes along the ventral visual stream, with most impaired activation changes in the mesial temporal lobe (MTL) and fusiform regions, most preserved activations in primary visual cortex and variably affected activations in secondary visual areas. Group-level correlations with behavioural measures of explicit memory were found in MTL, lingual and fusiform areas, whereas correlations with priming were found in lateral occipital, parietal and frontal areas. Together, these fMRI findings indicate a dissociation in Alzheimer's disease between impaired explicit memory encoding in MTL and fusiform regions and intact implicit encoding in earlier-stage occipital cortex.
Summarizing sporting events using twitter
(2012)

The status updates posted to social networks, such as Twitter and Facebook, contain a myriad of information about what people are doing and watching. During events, such as sports games, many updates are sent describing and expressing opinions about the event. In this paper, we describe an algorithm that generates a journalistic summary of an event using only status updates from Twitter as a source. Temporal cues, such as spikes in the volume of status updates, are used to identify the important moments within an event, and a sentence ranking method is used to extract relevant sentences from the corpus of status updates describing each important moment within an event. We evaluate our algorithm compared to human-generated summaries and the previous best summarization algorithm, and find that the results of our method are superior to the previous algorithm and approach the readability and grammaticality of the human-generated summaries.
Circulating Mitochondrial DAMPs Cause Inflammatory Responses to Injury
(2010)

Injury causes a systemic inflammatory response syndrome (SIRS) that is clinically much like sepsis. Microbial pathogen-associated molecular patterns (PAMPs) activate innate immunocytes through pattern recognition receptors. Similarly, cellular injury can release endogenous ‘damage’-associated molecular patterns (DAMPs) that activate innate immunity. Mitochondria are evolutionary endosymbionts that were derived from bacteria and so might bear bacterial molecular motifs. Here we show that injury releases mitochondrial DAMPs (MTDs) into the circulation with functionally important immune consequences. MTDs include formyl peptides and mitochondrial DNA. These activate human polymorphonuclear neutrophils (PMNs) through formyl peptide receptor-1 and Toll-like receptor (TLR) 9, respectively. MTDs promote PMN Ca2+ flux and phosphorylation of mitogen-activated protein (MAP) kinases, thus leading to PMN migration and degranulation in vitro and in vivo. Circulating MTDs can elicit neutrophil-mediated organ injury. Cellular disruption by trauma releases mitochondrial DAMPs with evolutionarily conserved similarities to bacterial PAMPs into the circulation. These signal through innate immune pathways identical to those activated in sepsis to create a sepsis-like state. The release of such mitochondrial ‘enemies within’ by cellular injury is a key link between trauma, inflammation and SIRS.
CNN vs. SIFT for Image Retrieval: Alternative or Complementary?
(2016)

In the past decade, SIFT is widely used in most vision tasks such as image retrieval. While in recent several years, deep convolutional neural networks (CNN) features achieve the state-of-the-art performance in several tasks such as image classification and object detection. Thus a natural question arises: for the image retrieval task, can CNN features substitute for SIFT? In this paper, we experimentally demonstrate that the two kinds of features are highly complementary. Following this fact, we propose an image representation model, complementary CNN and SIFT (CCS), to fuse CNN and SIFT in a multi-level and complementary way. In particular, it can be used to simultaneously describe scene-level, object-level and point-level contents in images. Extensive experiments are conducted on four image retrieval benchmarks, and the experimental results show that our CCS achieves state-of-the-art retrieval results.
Efficient Voltage Regulation for Microprocessor Cores Stacked in Vertical Voltage Domains
(2016)

Due to exponential (Moores law) scaling of advanced CMOS technologies, the challenges associated with delivering power to performance and mobile computing systems are outpacing the capabilities of conventional voltage regulator (VR) topologies. To continue to scale throughput at constant power density, the level of parallelism in microprocessor architectures is expected to increase substantially. In this paper, we present a power conversion topology to provide independent multicore regulation in the 0.8-1.4 V range from a 12-V dc bus. The topology uses a multistage ladder converter to manage power delivery to digital circuits stacked in vertical voltage domains. This approach has several advantages with regard to systems efficiency as it allows a more moderate conversion ratio of the main dc-dc converter. Moreover, the parallel converter only needs to process a fraction of the power of each core as the current can be “recycled” by adjacent cores in the stack. We develop a dynamical model for a multiple-input, multiple-output control scheme that uses a simple integral-control law, augmented with fast voltage- and current-mode feedforward. Measurement results of a discrete prototype verify the control scheme and demonstrate the potential advantages in system efficiency but also emphasize the remaining challenges in meeting stringent VR dynamic response requirements.
On Using Very Large Target Vocabulary for Neural Machine Translation
(2015)

Neural machine translation, a recently proposed approach to machine translation based purely on neural networks, has shown promising results compared to the existing approaches such as phrasebased statistical machine translation. Despite its recent success, neural machine translation has its limitation in handling a larger vocabulary, as training complexity as well as decoding complexity increase proportionally to the number of target words. In this paper, we propose a method based on importance sampling that allows us to use a very large target vocabulary without increasing training complexity. We show that decoding can be efficiently done even with the model having a very large target vocabulary by selecting only a small subset of the whole target vocabulary. The models trained by the proposed approach are empirically found to match, and in some cases outperform, the baseline models with a small vocabulary as well as the LSTM-based neural machine translation models. Furthermore, when we use an ensemble of a few models with very large target vocabularies, we achieve performance comparable to the state of the art (measured by BLEU) on both the English→German and English→French translation tasks of WMT’14.
Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation
(2016)

The attentional mechanism has proven to be effective in improving end-to-end neural machine translation. However, due to the intricate structural divergence between natural languages, unidirectional attention-based models might only capture partial aspects of attentional regularities. We propose agreement-based joint training for bidirectional attention-based end-to-end neural machine translation. Instead of training source-to-target and target-to-source translation models independently, our approach encourages the two complementary models to agree on word alignment matrices on the same training data. Experiments on ChineseEnglish and English-French translation tasks show that agreement-based joint training significantly improves both alignment and translation quality over independent training.
Alignment by Agreement
(2006)

We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32% reduction in AER. Moreover, a simple and efficient pair of HMM aligners provides a 29% reduction in AER over symmetrized IBM model 4 predictions. Disciplines Computer Sciences Comments Percy Liang, Ben Taskar, and Dan Klein. 2006. Alignment by agreement. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL '06). Association for Computational Linguistics, Stroudsburg, PA, USA, 104-111. DOI=10.3115/1220835.1220849 http://dx.doi.org/10.3115/1220835.1220849 © ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, {(2006)} http://doi.acm.org/10.3115/1220835.1220849" Email permissions@acm.org This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/533 Alignment by Agreement Percy Liang UC Berkeley Berkeley, CA 94720 pliang@cs.berkeley.edu Ben Taskar UC Berkeley Berkeley, CA 94720 taskar@cs.berkeley.edu Dan Klein UC Berkeley Berkeley, CA 94720 klein@cs.berkeley.edu
Fan-Beam Millimeter-Wave Antenna Design Based on the Cylindrical Luneberg Lens
(2007)

A new design of two-dimensional cylindrical Luneberg lens is introduced based on TE10 mode propagation between parallel plates, with special focus on ease of manufacturing. The parallel plates are partially filled with low cost polymer material (Rexolite epsivr = 2.54) to match Luneberg's law. A planar linear tapered slot antenna (LTSA) is inserted into the air region between the parallel plates at the edge of the Luneberg lens as a feed antenna, with fine positioning to the focal point of the Luneberg lens to optimize the antenna system performance. A combined ray-optics/diffraction method is used to obtain the radiation pattern of the system and results are compared with predictions of a time domain numerical solver. Measurements done on a 10-cm Luneberg lens designed for operation at 30 GHz agree very well with predictions. For this prototype, 3-dB E- and if-plane beamwidths of 6.6deg and 54deg respectively were obtained, and the sidelobe level in the E-plane was -17.7-dB. Although the parallel plate configuration should lead to a narrow band design due to the dispersion characteristics of the TE10 mode, the measurement results demonstrate broadband characteristics with radiation efficiencies varying between 43% and 72% over the tested frequency band of 26.5-37 GHz. The designed cylindrical Luneberg lens can be used to launch multiple beams by implementing an arc array of planar LTSA elements at the periphery of the lens, and can be easily extended to higher mm-wave frequencies.
SINGO: A single-end-operative and genderless connector for self-reconfiguration, self-assembly and self-healing
(2009)

Flexible and reliable connection is critical for self-reconfiguration, self-assembly, or self-healing. However, most existing connection mechanisms suffer from a deficiency that a connection would seize itself if one end malfunctions or is out of service. To mitigate this limitation on self-healing, this paper presents a new SINGO connector that can establish or disengage a connection even if one end of the connection is not operational. We describe the design and the prototype of the connector and demonstrate its performance by both theoretical analysis and physical experimentations.
Content matters: A study of hate groups detection based on social networks analysis and web mining
(2013)

In recent years, with rapid growth of social networking websites, users are very active in these platforms and large amount of data are aggregated. Among those social networking websites, Facebook is the most popular website that has most users. However, in Facebook, the abusing problem is a very critical issue, such as Hate Groups. Therefore, many researchers are devoting on how to detect potential hate groups, such as using the techniques of social networks analysis. However, we believe content is also a very important factors for hate groups detection. Thus, in this paper, we will propose an architecture to for hate groups detection which is based on the technique of Social Networks Analysis and Web Mining (Text Mining; Natural Language Processing). From the experiment result, it shows that content plays an critical role for hate groups detection and the performance is better than the system that just applying social networks analysis.
Occupational risks and challenges of seafaring.
(2010)

UNLABELLED
Seafarers are exposed to a high diversity of occupational health hazards onboard ships.


OBJECTIVE
The aim of this article is to present a survey of the current, most important hazards in seafaring including recommendations on measures how to deal with these problems.


METHODS
The review is based on maritime expert opinions as well a PubMed analysis related to the occupational risks of seafaring.


RESULTS
Despite recent advances in injury prevention, accidents due to harmful working and living conditions at sea and of non-observance of safety rules remain a main cause of injury and death. Mortality in seafaring from cardiovascular diseases (CVD) is mainly caused by increased risks and impaired treatment options of CVD at sea. Further, shipboard stress and high demand may lead to fatigue and isolation which have an impact on the health of onboard seafarers. Communicable diseases in seafaring remain an occupational problem. Exposures to hazardous substances and UV-light are important health risks onboard ships. Because of harsh working conditions onboard including environmental conditions, sufficient recreational activities are needed for the seafarers' compensation both onboard and ashore. However, in reality there is often a lack of leisure time possibilities.


DISCUSSION
Seafaring is still an occupation with specific work-related risks. Thus, a further reduction of occupational hazards aboard ships is needed and poses a challenge for maritime health specialists and stakeholders. Nowadays, maritime medicine encompasses a broad field of workplaces with different job-related challenges.
IoT Security: Ongoing Challenges and Research Opportunities
(2014)

The Internet of Things (IoT) opens opportunities for wearable devices, home appliances, and software to share and communicate information on the Internet. Given that the shared data contains a large amount of private information, preserving information security on the shared data is an important issue that cannot be neglected. In this paper, we begin with general information security background of IoT and continue on with information security related challenges that IoT will encountered. Finally, we will also point out research directions that could be the future work for the solutions to the security challenges that IoT encounters.
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
(2006)

In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., backpropagation for multilabel learning, is proposed. It is derived from the popular backpropagation algorithm through employing a novel error function capturing the characteristics of multilabel learning, i.e., the labels belonging to an instance should be ranked higher than those not belonging to that instance. Applications to two real-world multilabel learning problems, i.e., functional genomics and text categorization, show that the performance of BP-MLL is superior to that of some well-established multilabel learning algorithms
Investigating Information Systems with Action Research
(1999)

Action research is an established research method in use in the social and medical sciences since the mid-twentieth century, and has increased in importance for information systems toward the end of the 1990s. Its particular philosophic context is couched in strongly post-positivist assumptions such as idiographic and interpretive research ideals. Action research has developed a history within information systems that can be explicitly linked to early work by Lewin and the Tavistock Institute. Action research varies in form, and responds to particular problem domains. The most typical form is a participatory method based on a five-step model, which is exemplified by published IS research.
Chapter 2 Depression and a Stepped Care Model
(2010)

Given the public health significance of depression and the limited resources available for providing evidence-based treatment, there is a need to develop effective models of care to reduce the personal and societal costs of the disorder. Within stepped care service provisions, all patients presenting with symptoms of depression generally are first offered the lowest intensity and least intrusive intervention deemed necessary following assessment and triage. Only when patients do not show improvement do they move to higher, more intensive levels of care. However, stepped care models also provide information to aid clinicians in decision making regarding selection of treatment strategies that are most appropriate for an individual patient. For some individuals, lower levels of care would never be appropriate or may not be preferred by the consumer. Thus, stepped interventions offer a variety of treatment options to match the intensity of the patient’s presenting problem as well as potential patient preference. In this chapter, we discuss various strategies for treating depression consistent with a stepped model of care beginning with least intensive treatment and then moving up through the hierarchy of steps of care. While this is not a comprehensive review of all available treatments for depression, the chapter is designed to make clinicians aware of specific strategies for addressing depressive symptoms and to provide guidance about resources available at the various levels of care.